Applications and Development of Underwater Robotics and Underwater Vision Technology, 2nd Edition

Topic Information

Dear Colleagues,

This Topic, “Applications and Development of Underwater Robotics and Underwater Vision Technology, 2nd Edition” follows the success of its predecessor.

In today’s world, the ocean is one of the most important areas for human exploration and development. Underwater vision, as a cross-disciplinary field related to underwater environments, has a wide range of applications in marine resource development, marine biology research, underwater detection and control, and other fields.

In terms of marine resource development, underwater vision technology is a valuable tool for marine oil exploration and deep-sea mineral resource development. For example, high-precision underwater vision systems on underwater robots can facilitate oil exploration and development in deep-sea environments. Moreover, these robots can be utilized for the exploration and development of seabed mineral resources, enabling the development and utilization of deep-sea resources.

Regarding research on marine biology, underwater vision technology is useful for observing and studying marine organisms. High-definition underwater cameras on underwater robots can capture and observe marine organisms in the ocean. Furthermore, these cameras can aid in the study of deep-sea organisms, which enables scientists to comprehend the distribution of biological communities and ecosystems in deep-sea environments.

As for underwater detection and control, underwater vision technology plays a vital role in underwater target detection, underwater 3D modeling, and more. For example, underwater robots equipped with underwater sonars and cameras can detect and identify underwater targets in underwater environments. Moreover, these robots can create 3D models of underwater environments, providing a visualization tool for their detection and study.

Therefore, the application of underwater vision in the marine field has broad prospects for and holds great significance to promoting the development of the marine field. To promote the development of the underwater vision field, we will edit a Special Issue on underwater vision, inviting experts and scholars to share both their research results and the latest developments in the field.

We welcome submissions of papers related to the following areas:

• Underwater robot vision systems;
• Underwater image enhancement and processing techniques;
• Underwater object detection and recognition;
• Underwater 3D reconstruction techniques;
• Underwater optical imaging and laser scanning technologies;
• Underwater physical environment modeling and simulation;
• Underwater acoustic imaging and sonar technologies;
• Underwater communication and networking technologies.

Dr. Jingchun Zhou
Prof. Dr. Wenqi Ren
Prof. Dr. Qiuping Jiang
Dr. Yan-Tsung Peng
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Journal of Imaging jimaging	3.3	6.7	2015	15.3 Days	CHF 1800	Submit
Journal of Marine Science and Engineering jmse	2.8	5.0	2013	15.6 Days	CHF 2600	Submit
Machines machines	2.5	4.7	2013	16.9 Days	CHF 2400	Submit
Robotics robotics	3.3	7.7	2012	21.8 Days	CHF 1800	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Drones drones	4.8	7.4	2017	20.1 Days	CHF 2600	Submit

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Published Papers (2 papers)

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All Applied Sciences Electronics J. Imaging JMSE Machines Robotics Sensors Drones

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20 pages, 6887 KB  

Open AccessArticle

EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments

by Dehua Zou, Songhao Zhao, Jingchun Zhou, Guangqiang Liu, Zhiying Jiang, Minyi Xu, Xianping Fu and Siyuan Liu

J. Mar. Sci. Eng. 2025, 13(9), 1617; https://doi.org/10.3390/jmse13091617 (registering DOI) - 24 Aug 2025

Abstract

Marine benthic organism detection (BOD) is essential for underwater robotics and seabed resource management but suffers from motion blur, perspective distortion, and background clutter in dynamic underwater environments. To address visual feature degradation and computational constraints, we, in this paper, introduce EMR-YOLO, a [...] Read more.

Marine benthic organism detection (BOD) is essential for underwater robotics and seabed resource management but suffers from motion blur, perspective distortion, and background clutter in dynamic underwater environments. To address visual feature degradation and computational constraints, we, in this paper, introduce EMR-YOLO, a deep learning based multi-scale BOD method. To handle the diverse sizes and morphologies of benthic organisms, we propose an Efficient Detection Sparse Head (EDSHead), which combines a unified attention mechanism and dynamic sparse operators to enhance spatial modeling. For robust feature extraction under resource limitations, we design a lightweight Multi-Branch Fusion Downsampling (MBFDown) module that utilizes cross-stage feature fusion and multi-branch architecture to capture rich gradient information. Additionally, a Regional Two-Level Routing Attention (RTRA) mechanism is developed to mitigate background noise and sharpen focus on target regions. The experimental results demonstrate that EMR-YOLO achieves improvements of 2.33%, 1.50%, and 4.12% in AP, AP₅₀, and AP₇₅, respectively, outperforming state-of-the-art methods while maintaining efficiency. Full article

(This article belongs to the Topic Applications and Development of Underwater Robotics and Underwater Vision Technology, 2nd Edition)

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23 pages, 7739 KB  

Open AccessArticle

AGS-YOLO: An Efficient Underwater Small-Object Detection Network for Low-Resource Environments

by Weikai Sun, Xiaoqun Liu, Juan Hao, Qiyou Yao, Hailin Xi, Yuwen Wu and Zhaoye Xing

J. Mar. Sci. Eng. 2025, 13(8), 1465; https://doi.org/10.3390/jmse13081465 - 30 Jul 2025

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Abstract

Detecting underwater targets is crucial for ecological evaluation and the sustainable use of marine resources. To enhance environmental protection and optimize underwater resource utilization, this study proposes AGS-YOLO, an innovative underwater small-target detection model based on YOLO11. Firstly, this study proposes AMSA, a [...] Read more.

Detecting underwater targets is crucial for ecological evaluation and the sustainable use of marine resources. To enhance environmental protection and optimize underwater resource utilization, this study proposes AGS-YOLO, an innovative underwater small-target detection model based on YOLO11. Firstly, this study proposes AMSA, a multi-scale attention module, and optimizes the C3k2 structure to improve the detection and precise localization of small targets. Secondly, a streamlined GSConv convolutional module is incorporated to minimize the parameter count and computational load while effectively retaining inter-channel dependencies. Finally, a novel and efficient cross-scale connected neck network is designed to achieve information complementarity and feature fusion among different scales, efficiently capturing multi-scale semantics while decreasing the complexity of the model. In contrast with the baseline model, the method proposed in this paper demonstrates notable benefits for use in underwater devices constrained by limited computational capabilities. The results demonstrate that AGS-YOLO significantly outperforms previous methods in terms of accuracy on the DUO underwater dataset, with mAP@0.5 improving by 1.3% and mAP@0.5:0.95 improving by 2.6% relative to those of the baseline YOLO11n model. In addition, the proposed model also shows excellent performance on the RUOD dataset, demonstrating its competent detection accuracy and reliable generalization. This study proposes innovative approaches and methodologies for underwater small-target detection, which have significant practical relevance. Full article

(This article belongs to the Topic Applications and Development of Underwater Robotics and Underwater Vision Technology, 2nd Edition)

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